The field of road safety and safe driving has witnessed rapid advances due to improvements in sensing and computation technologies. Active safety features such as antilock braking systems and adaptive cruise control have widely been deployed in automobiles to reduce road accidents. However, the U.S. Department of Transportation (DOT) still classifies road safety as “a serious and national public health issue.” In 2008, road accidents in the U.S. caused 37,261 fatalities and about 2.35 million injuries. A particularly challenging driving task is negotiating traffic intersection safely. An estimated 45% of injury crashes and 22% of roadway fatalities in the U.S. are intersection related. A main contributing factor in these accidents is the inability of a driver to correctly assess and/or observe danger involved in such situations. These data suggest that driver assistance or warning systems may have an appropriate role in reducing the number of accidents, improving the safety and efficiency of human-driven ground transportation systems. Such systems typically augment the situational awareness of the driver and can also act as collision mitigation systems.
Research on intersection decision support systems has become quite active in both academia and the automotive industry. In the US, the federal DOT, in conjunction with the California, Minnesota, and Virginia DOTs, as well as several U.S. research universities, is sponsoring the Intersection Decision Support project and, more recently, the Cooperative Intersection Collision Avoidance Systems (CICAS) project. In Europe, the InterSafe project was created by the European Commission to increase safety at intersections. The partners in the InterSafe project include European vehicle manufacturers and research institutes. Both projects try to explore the requirements, tradeoffs, and technologies required to create an intersection collision avoidance system and demonstrate its applicability on selected dangerous scenarios.
Inferring driver intentions has been the subject of extensive research. For example, mind-tracking approaches have been introduced that extract the similarity of driver data to several virtual drivers created probabilistically using a cognitive model. In addition, other approaches have used graphical models and hidden Markov models (HMMs) to create and train models of different driver maneuvers using experimental driving data.
More specifically, the modeling of behavior at intersections has been studied using different statistical models. These studies have showed that the stopping at intersections behavior depends on several factors including driver profile (e.g., age and perception-reaction time) and yellow-onset kinematic and geometric parameters (e.g., vehicle speed and distance to intersection). One approach has developed red light running predictors based on estimating the time-to-arrival at intersections and the different stop-and-go maneuvers. It used speed measurements at two discrete point sensors, but the performance of this approach is limited by the complexity of the multidimensional optimization problem that must be solved.
A paper entitled “Intersection Decision Support: Evaluation of a Violation Warning System to Mitigate Striaght Crossing Path Crashes (report no. vtrc 06-cr10),” by V Neale. M. erez, Z. Doerzaph, S. Lee, S. Stone, and T. DingusVirginia Trans. Res. Council 2006, discusses the use of time-to-intersection (TTI) and its advantages over time-to-collision (TTC) for intersection safety systems. In addition, a paper entitled, “Cooperative intersection collision avoidance for violations: Threat assessment algorithm development and evaluation method,” by Z. Doerzaph, V. Neale, and R. Kiefer, presented at the Transportation Research Board 89th Annual Meeting, Washington, D.C., 2010, Paper 10-2748, illustrates how different warning algorithms are developed for signalized and stop intersections based on a required deceleration parameter (RDP), TTI, and speed-distance regression (SDR) models. It is noted, however, that these authors only consider very simple relationships between the driving parameters, and do not combine flexibility to combine many parameters in the same model.
Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.